Climatic resilience of wheat is its capacity to absorb disorder, maintain, self-organization while retaining the same basic structure and ways of functioning, adaptating to stress and change. The present studies were conducted with the objectives (a) to understand wheat crop biodynamism and its relation with yield under variable climatic conditions of Pothwar, (b) to parameterize and evaluate the APSIM-wheat module under variable local environmental conditions (c) to enhance understanding about the resilience of wheat under rainfed conditions using APSIM. Field trials on wheat were performed during two wheat growing environments from 2008 to 2010 at high (Islamabad), medium (Chakwal) and low (Talagang) rainfall zones of Pothwar. A Randomized complete block design was used and three wheat genotypes (Chakwal-50, Wafaq-2001 and GA-2002) were replicated four times among five planting windows denoted as PW''s. The PW‘s as management‘s starts from October and ended at the start of December. These five planting windows were denoted as PW1 (Sowing between 10-20 October), PW2 (Sowing between 21-31 October), PW3 (Sowing between 1-10 November), PW4 (Sowing between 11-20 November) and PW5 (Sowing between 01-10 December). Daily rainfall data (1961-2010) were obtained from the Pakistan Meteorological Department. Soil water dynamics recorded layer wise at pre-sowing, three leaf, tillering anthesis and maturity stages of crop showed significant variability at different locations among PW‘s and environments. Significant differences were observed in the pattern of emergence rate index (ERI), growth, drymatter accumulation, yield and grain quality of wheat crop at three locations among PW‘s and during two environments (2008-09 and 2009-10). The number of days for phenological development of crop (Z13, Z20, Z60 and Z92) remained statistically higher at Islamabad during 2008-09 as compared to other locations and second environment. Dry matter accumulation at three leaf and anthesis stage remained significantly different during two environments, three locations and planting windows. However, decline in drymatter at Talagang, late sowing and during 2009- 10 was due to prevailing stresses in the form of moisture and temperature. The xxvii trend of growth dynamics (crop growth rate (CGR) and net assimilation rate (NAR)) remained greater during 2008-09 for PW2 at Islamabad while lowest at Talagang and during 2009-10. The CGR and NAR reduction was recorded for all genotypes from Islamabad to Talagang moving from PW1 to PW5. Physiological parameters like stomatal conductance (gs), Stomatal resistance (rs), Net Photosynthetic rate (An), Transpiration rate (E) and Intercellular CO2 (Ci) were significantly affected due to treatments (environments, locations, PW‘s and genotypes). The maximum stomatal conductance recorded during 2008- 09 (0.639 mole m-2 sec-1) while minimum noted during 2009-10 (0.498 mole m-2 sec-1). However, stomatal resistance remained inverse to conductance among treatments. Similarly, significant variability in SPAD chlorophyll contents and proline concentration recorded at anthesis among treatments. Grain yield reduced for all genotypes with change in planting windows during two environments at three variable locations. Quantification of this yield to environmental variables depicted reduction (65 kg/ha/day) across the genotypes and environments. Similar trend was observed for other yield components. The Agricultural Production System Simulator (APSIM) was evaluated and parameterized as a bioinformatics tool using wheat crop, soil and climatic data of three locations of Pothwar. The optimization and modification of various genotype coefficients lead to goodness of fit between simulated and observed data regarding the occurrence of anthesis and maturity by accurately regulating the phasic development of the genotypes used in this study. This improvement in the models ability also improved the simulation capabilities of the model regarding days after sowing, leaf area index (LAI), drymatter accumulation, yield, harvest index (HI) and total soil water extraction across treatments. The validated crop simulation model was used as decision support tool for selecting optimum planting window and genotypes at different locations during variable environments. The simulation analysis regarding partioning of yield across PW‘s at different locations among genotypes showed that sowing after PW3 was more vulnerable to climate fluctuations governed by SOI phases in July. The long term APSIM analysis depicted that increase or decrease in wheat yield is linked with rainfall variability governed by Southern Oscillation Index (SOI) phases in July. The analysis using probabilistic approach based on long term rainfall variability revealed 44, 40 and 35 % possibility of exceeding median rain for Islamabad, Chakwal and Talagang when SOI phase in July is near zero, whereas possibility of exceeding 35, 34 and 33% possibility of exceeding median rain at Islamabad, Chakwal and Talagang is linked with negative SOI phase in July. Similarly, model forecasted monsoon (JAS), early wheat growing season (NDJ), end wheat growing season (FMA) and total growing season rainfall (NDJFMA) with good accuracy and skill scores. Further work is suggested to link climatic variability all over Pakistan with SOI and SSTs so that risk management tools can be used in the agricultural system management to maintained yield sustainability. Similarly use of simulation techniques like APSIM and ―R‖ as decision making tool based upon long term climatic data need to be used for development of agriculture sector.